Based on Mitchell's definition [1]}, a machine learns a task from an experience when its performance improves with training examples of the task. In other words, during the learning process, the learner can produce a hypothesis that performs well on future examples of the same task. This learning process is done based on the set of assumptions known as inductive bias [2]}. In many machine learning problems, finding methods for automatically learning the inductive bias is desirable. Meta learning also known as learning to learn [3]} formalizes this goal by observing data from a number of inherently related tasks. Then, it uses the gained experience and knowledge to learn appropriate bias which can be fine-tuned to perform well on new tasks.
Thus, the meta-learner speeds up the learning of a new, previously unseen task [2]}.
For instance, learning the initialization and the learning rate of a training algorithm [5]}, [6]}, the model architectures of a neural network [7]}, or the optimization algorithm of a neural network [8]}, all are within the scope of meta-learning.
